From 89c60a9e8d6d6f51d2900d3a4a46a88b9eb3d56a Mon Sep 17 00:00:00 2001 From: Dan Howe Date: Tue, 19 Jun 2018 14:06:26 +1000 Subject: [PATCH] Add usage examples to readme --- README.md | 156 +++++++++++++++++++++++++++----- doc/gridded-map-demo.png | Bin 0 -> 445946 bytes doc/gridded-timeseries-demo.png | Bin 0 -> 79686 bytes 3 files changed, 135 insertions(+), 21 deletions(-) create mode 100644 doc/gridded-map-demo.png create mode 100644 doc/gridded-timeseries-demo.png diff --git a/README.md b/README.md index e562139..c34d914 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,141 @@ Scientific Information for Land Owners (SILO) is a database of Australian climat https://silo.longpaddock.qld.gov.au -The climate variables are: +There are two different data types: + +#### Point data: +Get multiple climate variables at a single point (accessible with this python module). + +#### Gridded data: +Get a single climate variable for the entire Australian continent (accessible on the SILO website). + +## Point data ## + +### Installation ### + +``` +git clone http://git.wrl.unsw.edu.au:3000/danh/silo.git +pip install -e silo +``` + +## Usage ## +```python +import silo + +variables = ['daily_rain', 'max_temp', 'min_temp'] +api_key = 'BcfJnotlnVgX9gyLmZh2dURqNH8xAD3m8Am7V9OY' +start = '18990101' +finish = '20180101' +lat = -33 +lon = 151 + +silo.pointdata(variables, api_key, start, finish, lat=lat, lon=lon, + output='silo-data.csv') +``` + +**silo-data.csv** + +| date | latitude | longitude | daily_rain_mm | daily_rain_source | max_temp_Celsius | max_temp_source | min_temp_Celsius | min_temp_source | +|------------------------|----------|-----------|---------------|-------------------|------------------|-----------------|------------------|-----------------| +| 1899-01-01 | -33 | 151 | 0.9 | 24 | 31.4 | 35 | 14.8 | 35 | +| 1899-01-02 | -33 | 151 | 0 | 24 | 30.1 | 35 | 18.3 | 35 | +| 1899-01-03 | -33 | 151 | 0 | 24 | 33.3 | 35 | 14.3 | 35 | +| ... | | | | | | | | | + +## Gridded data ## + +Gridded datasets are available here: + +https://silo.longpaddock.qld.gov.au/gridded-data + +### Example: generate timeseries at specific location ### + +```python +import xarray as xr +import pandas as pd +import calendar +import matplotlib.pyplot as plt + +# Open netcdf dataset +fname = '2000.monthly_rain.nc' +ds = xr.open_dataset(fname) + +# Convert to pandas dataframe +df = ds.to_dataframe().drop(columns=['crs']) + +# Set coordinates +lat = -33.75 +lon = 151.25 + +# Create temporary dataframe at specific location +df1 = df.loc[lat, lon] + +# Plot time series +fig, ax = plt.subplots(1, 1, figsize=(6, 4)) +ax.plot(df1.index, df1.values) + +# Tidy up figure +ax.set_xticks(df1.index) +ax.set_xticklabels([calendar.month_abbr[i] for i in df1.index.month]) +ax.set_ylabel('Rainfall (mm)', labelpad=10) +ax.spines['right'].set_visible(False) +ax.spines['top'].set_visible(False) + +plt.show() +``` + + +### Example: generate maps at specific dates ### + +```python +import xarray as xr +import pandas as pd +import calendar +import matplotlib.pyplot as plt + +# Open netcdf dataset +fname = '2000.monthly_rain.nc' +ds = xr.open_dataset(fname) + +# Convert to pandas dataframe +df = ds.to_dataframe().drop(columns=['crs']) + +# Get index values +lats = df.index.get_level_values('lat').unique() +lons = df.index.get_level_values('lon').unique() +dates = df.index.get_level_values('time').unique() + +fig, ax = plt.subplots(3, 4) + +for i, date in enumerate(dates): + # Get temporary dataframe with only one date + df1 = df[df.index.get_level_values('time') == date] + + # Remove date from multi-index + df1.index = df1.index.droplevel(-1) + + # Split multi-index so that rows=latitude and columns=longitude + grid = df1.unstack() + + # Plot colour map for current month + a = ax.ravel()[i] + im = a.imshow(grid, cmap='Blues', vmin=-100, vmax=800) + a.set_title(calendar.month_abbr[i + 1]) + a.invert_yaxis() + a.axis('off') + +# Add colour bar +cbax = fig.add_axes([0.95, 0.3, 0.02, 0.4]) +cb = fig.colorbar(im, cax=cbax) +cb.set_ticks([0, 200, 400, 600, 800]) +cbax.set_ylabel('Monthly rainfall (mm)', labelpad=10) + +plt.show() +``` + + + +## Available climate variables ## | code | name | units | |---------------------|-----------------------------------------------------------------------------------------|---------| @@ -27,23 +161,3 @@ The climate variables are: | et_morton_potential | Evapotranspiration - Morton's potential evapotranspiration | mm | | et_morton_wet | Evapotranspiration - Morton's wet-environment areal evapotranspiration over land | mm | | mslp | Mean sea level pressure | hPa | - -There are two different data types: -1. Point data (accessible with this python module) -2. 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